The use of artificial neural networks which are trained on the basis of training data comprising appropriate states of the technical system for modeling a technical system is known from the prior art. The trained neural network is then suitable for estimating or predicting states of the technical system.
Neural networks enable the prediction of states of an output layer on the basis of states of an input layer, where one or more neural layers having hidden states are provided between output layer and input layer. The greater the number of hidden layers used, the more accurately a technical system can be modeled by means of training a neural network. In this situation the problem does however exist that conventional monitored training methods, in particular the gradient descent method, are unsuitable for neural networks having a plurality of hidden layers. As a result, neural networks having a plurality of hidden layers are often trained in layer fashion using unmonitored training methods, where the adjustment to the actual states in accordance with the training data takes place subsequently using a gradient descent method.
The publication PETERSON, C. et al.: JETNET 3.0—A versatile artificial neural network package, Computer Physics Communications, Vol. 81, 1993, No. 1-2, pp. 185-220, relates to the description of a program package which is based on neural networks. In this situation the use of so-called skip-layer networks comprising skip-layer connections is also described.
The publication ROSENBERG, A.: Lecture 14—Neural Networks, Machine Learning, Queens College (CUNY), New York, USA, March 2010, discloses the structure of a skip-layer network in which an input layer is connected directly to an output layer by way of connectors.
The publication REYFUS G., Neural Networks, Methodoloy and Applications, 2nd edition, Berlin, Springer, 2005. pp. i, iii-xvi, 1-11, 171-186, discloses inter alia how a recurrent neural network can be represented in the canonical form of a feed-forward network. Several outputs from this feed-forward network are fed back to the inputs of the network.